In machine learning, a machine learned model is trained to infer a function from a collection of training data including features representing various aspects of the data. Oftentimes, features representing input data in a nominal or non-numeric manner are captured in the initial training data. Nominal values, however, can be difficult to use to generate a machine learned model. As a result, a nominal feature(s) is generally transformed or converted to a set of Boolean features. For example, a set of Boolean features that correspond with the number of possible values for a single nominal feature might be created. Converting a single nominal feature into a set of Boolean features to represent each potential value of the nominal feature can significantly increase the size of the input data and thereby impact storage and processing associated with machine learned models.